Remote sensing image building detection method based on Mask R-CNN
Qinzhe Han, Qian Yin, Xin Zheng, Ziyi Chen
Abstract
Abstract Quickly and conveniently identifying buildings in disaster areas plays an important role in disaster assessment. To achieve the technical requirements of flood disaster relief projects, this paper proposes a building extraction method for use with remote sensing images that combines traditional digital image processing methods and convolution neural networks. First, the threshold segmentation method is used to select and construct a training dataset. Then, a variety of preprocessing methods are used to enhance the selected dataset. Finally, the improved Mask R-CNN algorithm is used to detect buildings in the images. Experiments show that compared to the R-CNN algorithm, the proposed method improves detection accuracy and reduces the computational time.